Python 3.6.3 |Anaconda custom (64-bit)| (default, Oct 13 2017, 12:02:49)
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IPython 6.1.0 -- An enhanced Interactive Python.
Restarting kernel...
In [1]: runfile('/home/anand/UvA/Year 2/Period 2/NLP/NLP2017/Project/NLP1-2017-VQA/Codes/predict_test.py', wdir='/home/anand/UvA/Year 2/Period 2/NLP/NLP2017/Project/NLP1-2017-VQA/Codes')
CUDA: True
loading saved model
Predicting on Training Images
Question = ['how', 'many', 'slices', 'have', 'been', 'cut?']
Correct Answer = 1
Predicted Answer = 1
Question = ['how', 'many', 'rows', 'of', 'donuts', 'are', 'there', 'on', 'the', 'top', 'shelf?']
Correct Answer = 8
Predicted Answer = 8
Question = ['how', 'many', 'keyboards', 'can', 'be', 'seen?']
Correct Answer = 1
Predicted Answer = 1
Question = ['how', 'many', 'pillows', 'are', 'in', 'the', 'room?']
Correct Answer = 5
Predicted Answer = 6
Question = ['how', 'many', 'people', 'are', 'in', 'the', 'picture?']
Correct Answer = 2
Predicted Answer = 1
Question = ['what', 'color', 'is', 'the', 'sky?']
Correct Answer = gray
Predicted Answer = gray
Question = ['is', 'that', 'zebra', 'making', 'a', 'bowel', 'movement', 'mess', 'on', 'the', 'ground?']
Correct Answer = yes
Predicted Answer = yes
Question = ['how', 'many', 'people', 'are', 'touching', 'a', 'ball?']
Correct Answer = 0
Predicted Answer = 0
Question = ['is', 'this', 'a', 'healthy', 'meal?']
Correct Answer = yes
Predicted Answer = yes
Question = ['what', 'number', 'is', 'the', 'batter?']
Correct Answer = 16
Predicted Answer = 15
Predicting on Test Images
Question = ['where', 'is', 'the', 'woman', 'standing?']
Correct Answer = sidewalk
Predicted Answer = 11:30
Question = ['what', 'fruit', 'is', 'to', 'the', 'right', 'of', 'the', 'avocado?']
Correct Answer = lemons
Predicted Answer = yellow
Question = ['is', 'this', 'outside', 'or', 'in?']
Correct Answer = outside
Predicted Answer = 0
Question = ['is', 'this', 'a', 'large', 'truck?']
Correct Answer = yes
Predicted Answer = yes
Question = ['is', 'this', 'pizza', 'vegetarian?']
Correct Answer = yes
Predicted Answer = eating
Question = ['how', 'many', 'light', 'haired', 'colored', 'people', 'do', 'you', 'see?']
Correct Answer = 0
Predicted Answer = yes
Question = ['what', 'is', 'the', 'percentage', 'of', 'black', 'fur', 'to', 'white', 'fur', 'on', 'the', 'cat?']
Correct Answer = 50
Predicted Answer = 1949
Question = ['is', 'the', 'fridge', 'crowded?']
Correct Answer = yes
Predicted Answer = sandwich
Question = ['where', 'is', 'location?']
Correct Answer = kitchen
Predicted Answer = no
Question = ['how', 'many', 'sides', 'are', 'there', 'to', 'the', 'dish?']
Correct Answer = 3
Predicted Answer = window
In [2]: runfile('/home/anand/UvA/Year 2/Period 2/NLP/NLP2017/Project/NLP1-2017-VQA/Codes/predict_test.py', wdir='/home/anand/UvA/Year 2/Period 2/NLP/NLP2017/Project/NLP1-2017-VQA/Codes')
Reloaded modules: cbow
CUDA: True
loading saved model
Predicting on Training Images
Question = ['what', 'is', 'both', 'orange', 'and', 'red?']
Correct Answer = fire
Predicted Answer = blue
Question = ['where', 'is', 'the', 'sink?']
Correct Answer = kitchen
Predicted Answer = kitchen
Question = ['how', 'many', 'men', 'are', 'in', 'this', 'picture?']
Correct Answer = 2
Predicted Answer = 3
Question = ['is', 'there', 'something', 'on', 'top', 'of', 'the', 'jar?']
Correct Answer = no
Predicted Answer = no
Question = ['is', 'the', 'plane', 'going', 'to', 'land', 'soon?']
Correct Answer = yes
Predicted Answer = yes
Question = ['what', 'color', 'is', 'the', 'elephant?']
Correct Answer = gray
Predicted Answer = white
Question = ['how', 'many', 'animals', 'are', 'in', 'the', 'background?']
Correct Answer = 4
Predicted Answer = 2
Question = ['what', 'is', 'in', 'the', 'glass?']
Correct Answer = orange juice
Predicted Answer = orange juice
Question = ['what', 'color', 'is', 'the', 'goose?']
Correct Answer = gray
Predicted Answer = gray
Question = ['how', 'many', 'motorcycles', 'are', 'there?']
Correct Answer = 1
Predicted Answer = 1
Predicting on Test Images
Question = ['what', 'is', 'the', 'bank', 'on', 'the', 'sign?']
Correct Answer = hsbc
Predicted Answer = white
Question = ['does', 'this', 'look', 'like', 'mother', 'and', 'child?']
Correct Answer = yes
Predicted Answer = elephant
Question = ['how', 'many', 'blades', 'of', 'grass', 'is', 'the', 'giraffe', 'standing', 'on?']
Correct Answer = 100
Predicted Answer = standing
Question = ['is', 'this', 'a', 'cordless', 'mouse?']
Correct Answer = no
Predicted Answer = no
Question = ['how', 'many', 'oranges', 'are', 'there?']
Correct Answer = 3
Predicted Answer = yes
Question = ['is', 'the', 'roof', 'made', 'of', 'tin?']
Correct Answer = no
Predicted Answer = parking lot
Question = ['how', 'many', 'towels', 'are', 'there?']
Correct Answer = 2
Predicted Answer = yes
Question = ['is', 'the', 'street', 'well', 'paved?']
Correct Answer = no
Predicted Answer = airport
Question = ['how', 'many', 'different', 'colors', 'of', 'bananas', 'are', 'there?']
Correct Answer = 3
Predicted Answer = yes
Question = ['how', 'many', 'utensils', 'are', 'in', 'the', 'table?']
Correct Answer = 4
Predicted Answer = pizza
In [3]: